二氧化碳储存通量对森林生态系统碳收支的影响

涡度相关系统观测高度以下的CO₂储存通量对准确评价森林生态系统与大气间净CO₂交换量（NEE）有着重要的影响.本研究以长白山阔叶红松林为研究对象,利用2003年的涡度相关观测数据以及CO₂浓度廓线数据,分析了CO₂储存通量的变化规律及其对碳收支过程的影响.结果表明：涡度相关观测高度以下的CO₂储存通量具有典型的日变化特征,其最大变化量出现在大气稳定与不稳定层结转换期.利用涡度相关系统观测的单点CO₂浓度变化方法与利用CO₂浓度廓线方法计算的CO₂储存通量差异不显著.忽略CO₂储存通量,在半小时尺度上会造成对夜间和白天的NEE分别低估25%和19%,在日和年尺度上,会对NEE低估10%和25%;忽略CO₂储存通量,会低估Michaelis-Menten光响应方程及Lloyd-Taylor呼吸方程的参数,并且对表观初始量子效率α和参考呼吸R_ref的低估最大;忽略CO₂储存通量,在半小时、日及年尺度上,均会对总光合作用（GPP）和生态系统呼吸（R_e）低估约20%.     

华北平原冬小麦农田生态系统CO2通量特征及其影响因素

利用2014—2015年中国科学院封丘农业生态实验站涡度相关系统观测的冬小麦农田生态系统CO₂通量数据,结合试验地常规气象观测系统的气象数据,分析冬小麦4个生育期(分蘖期、越冬期、拔节期和灌浆期)内CO₂通量的日变化,研究净生态系统碳交换(NEE)的季节变化及其与气象要素的关系.结果表明: 冬小麦整个生育期内NEE为-360.15 g C·m^-2,总初级生产力总量为1920.01 g C·m^-2,冬小麦农田生态系统具有较强的固碳能力.冬小麦农田生态系统CO₂通量具有明显的日变化和季节变化特征,分蘖期表现为碳源,越冬期、拔节期和灌浆期表现为碳汇.表观初始光能利用率平均值为0.03 mg CO₂·μmol^-1,光饱和时的生态系统生产量平均值为1.53 mg CO₂·m^-2·s^-1,月平均生态系统呼吸为193.92 g C·m^-2·month^-1.冬小麦农田生态系统4个生育期NEE与光合有效辐射的相关关系均达到极显著水平.分蘖期、拔节期和灌浆期NEE与饱和水汽压差的相关关系极显著,越冬期达显著水平.冬小麦分蘖期、越冬期和灌浆期NEE日总量与土壤温度呈正相关,拔节期呈负相关关系.     

黄河三角洲芦苇湿地生长季净生态系统CO2交换及其环境调控机制

采用涡度相关法,对2011年生长季的黄河三角洲芦苇湿地净生态系统CO₂交换(NEE)进行了观测,研究湿地NEE的变化规律及其影响因子.结果表明: 不同月份芦苇湿地的NEE日变化均呈“U”形曲线,CO₂最大净吸收率和释放率的日均值分别为(0.44±0.03)和(0.16±0.01) mg CO₂·m^-2·s^-1;芦苇湿地NEE、生态系统呼吸(R_eco)、总初级生产力(GPP)的季节变化均呈现生长旺季(7-9月)较高、生长初期(5-6月)和生长末期(10-11月)较低的趋势;R_eco和NEE在8月达到峰值,GPP在7月达到峰值.芦苇湿地生态系统的CO₂交换受到光合有效辐射(PAR)、土壤温度(T_s)和土壤体积含水量(SWC)的共同影响.白天NEE与PAR呈直角双曲线关系;5 cm深处T_s与夜间生态系统呼吸(R_eco,n)呈指数关系,生态系统呼吸的温度敏感性(Q₁₀)为2.30,SWC和T_s是影响芦苇湿地R_eco,n的主要因子.在整个生长季,黄河三角洲芦苇湿地生态系统是一个明显的CO₂的汇,总净固碳量为780.95 g CO₂·m^-2.     

植物光合与呼吸过程CO2及其δ13C的变异特征与影响因素研究进展

植物光合与呼吸作用是生态系统碳循环的关键过程，其CO₂ δ¹³C的变异特征与影响因素分析是解析植物碳分配模式、代谢关键过程以及生态系统光合与呼吸组分拆分的理论基础。然而，关于不同时间尺度上光合与呼吸CO₂及其δ¹³C的变异特征及其影响因素仍然缺少清晰认识。从植物碳过程及其同位素效应、观测技术与方法、变异特征与影响因素以及应用实践与研究进展等方面论述了植物光合与呼吸碳过程及其δ¹³C的时间变化特征，揭示了气孔、叶肉和羧化限制等生物因素以及温度、湿度和光照等非生物因素对碳过程及其同位素效应的影响。总结了存在的问题，并展望了未来的研究方向，以期为今后该领域的研究提供理论依据和有效支撑。     

箱式通量观测技术和方法的理论假设及其应用进展

碳(CO₂、CH₄)、氮(N₂O)和水汽(H₂O)等温室气体的交换通量是生态系统物质循环的核心, 是地圈-生物圈-大气圈相互作用的纽带。稳定同位素光谱和质谱技术和方法的进步使碳稳定同位素比值(δ ¹³C)和氧稳定同位素比值(δ ¹⁸O)(CO₂)、δ ¹³C (CH₄)、氮稳定同位素比值(δ ¹⁵N)和δ ¹⁸O (N₂O)、氢稳定同位素比值(δD)和δ ¹⁸O (H₂O)的观测成为可能, 与箱式通量观测技术和方法结合可以实现土壤、植物乃至生态系统尺度温室气体及其同位素通量观测研究。该综述以CO₂及其δ ¹³C通量的箱式观测技术和方法为例, 概述了箱式通量观测系统的基本原理及分类, 阐述了系统设计的理论要求和假设, 综述了从野外到室内土壤、植物叶-茎-根以及生态系统尺度箱式通量观测研究的应用进展及问题, 展望了气体分析精度和准确度、观测数据精度和准确度以及观测数据的代表性评价在箱式通量观测研究中的重要性。     

潮汐作用下干湿交替对黄河三角洲盐沼湿地净生态系统CO2交换的影响

潮汐作用作为盐沼湿地独特的水文特征能在短时间内强烈影响盐沼湿地的碳平衡.利用涡度相关和微气象监测技术,对黄河三角洲盐沼湿地净生态系统CO₂交换(NEE)和环境因子进行监测,并同步监测潮汐变化,探究潮汐过程及潮汐作用下干湿交替对NEE的影响.结果表明: 潮汐过程促进了白天生态系统CO₂的吸收但未对夜晚CO₂的释放产生显著影响,潮汐淹水成为影响白天NEE的主要因子.干旱阶段和湿润阶段NEE的日平均动态均呈“U”型曲线,但干旱阶段NEE的变幅较小.干湿交替增强了白天生态系统CO₂的吸收,干旱阶段最大光合速率(A_max)、表观量子产量(α)和生态系统呼吸(R_eco)的均值均高于湿润阶段.此外,干湿交替减少了盐沼湿地夜晚NEE释放的同时增强了其温度敏感性.     

潮汐作用下干湿交替对黄河三角洲盐沼湿地净生态系统CO2交换的影响

潮汐作用作为盐沼湿地独特的水文特征能在短时间内强烈影响盐沼湿地的碳平衡.利用涡度相关和微气象监测技术,对黄河三角洲盐沼湿地净生态系统CO₂交换(NEE)和环境因子进行监测,并同步监测潮汐变化,探究潮汐过程及潮汐作用下干湿交替对NEE的影响.结果表明: 潮汐过程促进了白天生态系统CO₂的吸收但未对夜晚CO₂的释放产生显著影响,潮汐淹水成为影响白天NEE的主要因子.干旱阶段和湿润阶段NEE的日平均动态均呈“U”型曲线,但干旱阶段NEE的变幅较小.干湿交替增强了白天生态系统CO₂的吸收,干旱阶段最大光合速率(A_max)、表观量子产量(α)和生态系统呼吸(R_eco)的均值均高于湿润阶段.此外,干湿交替减少了盐沼湿地夜晚NEE释放的同时增强了其温度敏感性.     

黄河三角洲芦苇湿地生长季净生态系统CO2交换及其环境调控机制

采用涡度相关法,对2011年生长季的黄河三角洲芦苇湿地净生态系统CO₂交换(NEE)进行了观测,研究湿地NEE的变化规律及其影响因子.结果表明: 不同月份芦苇湿地的NEE日变化均呈“U”形曲线,CO₂最大净吸收率和释放率的日均值分别为(0.44±0.03)和(0.16±0.01) mg CO₂·m^-2·s^-1;芦苇湿地NEE、生态系统呼吸(R_eco)、总初级生产力(GPP)的季节变化均呈现生长旺季(7—9月)较高、生长初期(5—6月)和生长末期(10—11月)较低的趋势;R_eco和NEE在8月达到峰值,GPP在7月达到峰值.芦苇湿地生态系统的CO₂交换受到光合有效辐射(PAR)、土壤温度(T_s)和土壤体积含水量(SWC)的共同影响.白天NEE与PAR呈直角双曲线关系;5 cm深处T_s与夜间生态系统呼吸(R_eco,n)呈指数关系,生态系统呼吸的温度敏感性(Q₁₀)为2.30,SWC和T_s是影响芦苇湿地R_eco,n的主要因子.在整个生长季,黄河三角洲芦苇湿地生态系统是一个明显的CO₂的汇,总净固碳量为780.95 g CO₂·m^-2.     

环境和生物因子对黄河三角洲滨海湿地净生态系统CO2交换的影响

通过涡度相关和微气象观测技术,对黄河三角洲滨海湿地净生态系统CO₂交换(NEE)以及环境、生物因子进行了观测,探究湿地NEE变化规律及环境和生物因子对NEE的影响. 结果表明: 在日尺度上,生长季NEE呈明显“U”型曲线,非生长季变幅较小;在季节尺度上,NEE生长季波动较大,表现为碳汇,非生长季波动较小,表现为碳源;在年尺度上,滨海湿地生态系统表现为碳汇,总净固碳量为-247 g C·m^-2. 白天NEE主要受控于光合有效辐射(PAR),且生态系统表观量子产量(α)与白天生态系统呼吸(R_eco,d)均于8月达到最大值,最大光合速率(A_max)于7月达到最大值;夜间NEE随气温(T_a)呈指数增加趋势,生态系统的温度敏感系数(Q₁₀)为2.5,且土壤含水量(SWC)越高,Q₁₀值越大.非生长季NEE只与净辐射(R_n)呈显著的线性负相关,与其他环境因子无显著相关关系.生长季NEE与R_n、T_a、土壤10 cm温度(T_{s 10})等环境因子以及叶面积指数(LAI)呈显著的线性负相关,但与地上生物量(AGB)无显著相关关系.多元回归分析表明,R_n和LAI对生长季NEE的协同影响达到52%.     

我国亚热带毛竹林CO2通量的变异特征

2010年12月至2011年11月,利用涡度相关技术研究了我国亚热带(浙江)毛竹林生态系统的CO₂通量,分析了毛竹林净生态系统交换量(NEE)、生态系统呼吸量(RE)和生态系统总交换量(GEE)的变化.结果表明: 研究期间,毛竹林各月的NEE均为负值,7月最大,为-99.33 g C·m^-2,11月最小,仅-23.49 g C·m^-2,其变化曲线呈双峰型.各月CO₂通量平均日变化差异明显,9月最大,为-0.60 g CO₂·m^-2·s^-1,1月最小,为-0.30 g CO₂·m^-2·s^-1,且在NEE正负转换的时间点上呈明显的季节变化特征;全年RE呈单峰型变化,夏季最高、冬季最低,夜间RE与土壤温度呈极显著正相关.全年NEE、RE和GEE分别为-668.40、932.55和-1600.95 g C·m^-2·a^-1,NEE占GEE的41.8%.与其他生态系统相比,毛竹林的固碳能力极强.     

Using continuous stable isotope measurements to partition net ecosystem CO2 exchange

Ecosystem-scale estimation of photosynthesis and respiration using micrometeorological techniques remains an important, yet difficult, challenge. In this study, we combined micrometeorological and stable isotope methods to partition net ecosystem CO2 exchange (FN) into photosynthesis (F(A)) and respiration (F(R)) in a corn-soybean rotation ecosystem during the summer 2003 corn phase. Mixing ratios of (12)CO2 and (13)CO2 were measured continuously using tunable diode laser (TDL) absorption spectroscopy. The dynamics of the isotope ratio of ecosystem respiration (R), net ecosystem CO2 exchange (deltaN) and photosynthetic discrimination at the canopy scale (delta canopy) were examined. During the period of full canopy closure, F(N) was partitioned into photosynthesis and respiration using both the isotopic approach and the conventional night-time-derived regression methodology. Results showed that deltaR had significant seasonal variation (-32 to -11% per hundred) corresponding closely with canopy phenology. Daytime deltaN typically varied from -12 to -4% per hundred, while delta canopy remained relatively constant in the vicinity of 3% per hundred. Compared with the regression approach, the isotopic flux partitioning showed more short-term variations and was considerably more symmetric about F(N). In this experiment, the isotopic partitioning resulted in larger uncertainties, most of which were caused by the uncertainties in deltaN. and the daytime estimate of deltaR. By sufficiently reducing these uncertainties, the tunable diode laser (TDL)-micrometeorological technique should yield a better understanding of the processes controlling photosynthesis, respiration and ecosystem-scale discrimination.     

Evaluating the carbon balance estimate from an automated ground‐level flux chamber system in artificial grass mesocosms

Measuring and modeling carbon (C) stock changes in terrestrial ecosystems are pivotal in addressing global C‐cycling model uncertainties. Difficulties in detecting small short‐term changes in relatively large C stocks require the development of robust sensitive flux measurement techniques. Net ecosystem exchange (NEE) ground‐level chambers are increasingly used to assess C dynamics in low vegetation ecosystems but, to date, have lacked formal rigorous field validation against measured C stock changes. We developed and deployed an automated and multiplexed C‐flux chamber system in grassland mesocosms in order rigorously to compare ecosystem total C budget obtained using hourly C‐flux measurements versus destructive net C balance. The system combines transparent NEE and opaque respiration chambers enabling partitioning of photosynthetic and respiratory fluxes. The C‐balance comparison showed good agreement between the two methods, but only after NEE fluxes were corrected for light reductions due to chamber presence. The dark chamber fluxes allowed assessing temperature sensitivity of ecosystem respiration (R_eco) components (i.e., heterotrophic vs. autotrophic) at different growth stages. We propose that such automated flux chamber systems can provide an accurate C balance, also enabling pivotal partitioning of the different C‐flux components (e.g., photosynthesis and respiration) suitable for model evaluation and developments.     

Model-data synthesis of diurnal and seasonal CO2 fluxes at Niwot Ridge, Colorado

Eddy covariance records hold great promise for understanding the processes controlling the net ecosystem exchange of CO₂ (NEE). However, NEE is the small difference between two large fluxes: photosynthesis and ecosystem respiration. Consequently, separating NEE into its component fluxes, and determining the process‐level controls over these fluxes, is a difficult problem. In this study, we used a model‐data synthesis approach with the Simplified PnET (SIPNET) flux model to extract process‐level information from 5 years of eddy covariance data at an evergreen forest in the Colorado Rocky Mountains. SIPNET runs at a twice‐daily time step, and has two vegetation carbon pools, a single aggregated soil carbon pool, and a soil moisture submodel that models both evaporation and transpiration. By optimizing the model parameters before evaluating model‐data mismatches, we were able to probe the model structure independent of any arbitrary parameter set. In doing so, we were able to learn about the primary controls over NEE in this ecosystem, and in particular the respiration component of NEE. We also used this parameter optimization, coupled with a formal model selection criterion, to investigate the effects of making hypothesis‐driven changes to the model structure. These experiments lent support to the hypotheses that (1) photosynthesis, and possibly foliar respiration, are down‐regulated when the soil is frozen and (2) the metabolic processes of soil microbes vary in the summer and winter, possibly because of the existence of distinct microbial communities at these two times. Finally, we found that including water vapor fluxes, in addition to carbon fluxes, in the parameter optimization did not yield significantly more information about the partitioning of NEE into gross photosynthesis and ecosystem respiration.     

Partitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks

The eddy covariance (EC) technique is used to measure the net ecosystem exchange (NEE) of CO₂ between ecosystems and the atmosphere, offering a unique opportunity to study ecosystem responses to climate change. NEE is the difference between the total CO₂ release due to all respiration processes (RECO), and the gross carbon uptake by photosynthesis (GPP). These two gross CO₂ fluxes are derived from EC measurements by applying partitioning methods that rely on physiologically based functional relationships with a limited number of environmental drivers. However, the partitioning methods applied in the global FLUXNET network of EC observations do not account for the multiple co‐acting factors that modulate GPP and RECO flux dynamics. To overcome this limitation, we developed a hybrid data‐driven approach based on combined neural networks (NN_C‐part). NN_C‐part incorporates process knowledge by introducing a photosynthetic response based on the light‐use efficiency (LUE) concept, and uses a comprehensive dataset of soil and micrometeorological variables as fluxes drivers. We applied the method to 36 sites from the FLUXNET2015 dataset and found a high consistency in the results with those derived from other standard partitioning methods for both GPP (R² > .94) and RECO (R² > .8). High consistency was also found for (a) the diurnal and seasonal patterns of fluxes and (b) the ecosystem functional responses. NN_C‐part performed more realistic than the traditional methods for predicting additional patterns of gross CO₂ fluxes, such as: (a) the GPP response to VPD, (b) direct effects of air temperature on GPP dynamics, (c) hysteresis in the diel cycle of gross CO₂ fluxes, (d) the sensitivity of LUE to the diffuse to direct radiation ratio, and (e) the post rain respiration pulse after a long dry period. In conclusion, NN_C‐part is a valid data‐driven approach to provide GPP and RECO estimates and complementary to the existing partitioning methods.     

Partitioning net ecosystem carbon exchange with isotopic fluxes of CO2

Because biological and physical processes alter the stable isotopic composition of atmospheric CO₂, variations in isotopic content can be used to investigate those processes. Isotopic flux measurements of ¹³CO₂ above terrestrial ecosystems can potentially be used to separate net ecosystem CO₂ exchange (NEE) into its component fluxes, net photosynthetic assimilation (F_A) and ecosystem respiration (F_R). In this paper theory is developed to partition measured NEE into F_A and F_R, using measurements of fluxes of CO₂ and ¹³CO₂, and isotopic composition of respired CO₂ and forest air. The theory is then applied to fluxes measured (or estimated, for ¹³CO₂) in a temperate deciduous forest in eastern Tennessee (Walker Branch Watershed). It appears that there is indeed enough additional information in ¹³CO₂ fluxes to partition NEE into its photosynthetic and respiratory components. Diurnal patterns in F_A and F_R were obtained, which are consistent in magnitude and shape with patterns obtained from NEE measurements and an exponential regression between night‐time NEE and temperature (a standard technique which provides alternate estimates of F_R and F_A). The light response curve for photosynthesis (F_A vs. PAR) was weakly nonlinear, indicating potential for saturation at high light intensities. Assimilation‐weighted discrimination against ¹³CO₂ for this forest during July 1999 was 16.8–17.1‰, depending on canopy conductance. The greatest uncertainties in this approach lie in the evaluation of canopy conductance and its effect on whole‐canopy photosynthetic discrimination, and thus the indirect methods used to estimate isotopic fluxes. Direct eddy covariance measurements of ¹³CO₂ flux are needed to assess the validity of the assumptions used and provide defensible isotope‐based estimates of the component fluxes of net ecosystem exchange.     

Elevated CO(2) and temperature alter net ecosystem C exchange in a young Douglas fir mesocosm experiment

We investigated the effects of elevated CO(2) (EC) [ambient CO(2) (AC) + 190 ppm] and elevated temperature (ET) [ambient temperature (AT) + 3.6 degrees C] on net ecosystem exchange (NEE) of seedling Douglas fir (Pseudotsuga menziesii) mesocosms. As the study utilized seedlings in reconstructed soil-litter-plant systems, we anticipated greater C losses through ecosystem respiration (R(e)) than gains through gross photosynthesis (GPP), i.e. negative NEE. We hypothesized that: (1) EC would increase GPP more than R(e), resulting in NEE being less negative; and (2) ET would increase R(e) more than GPP, resulting in NEE being more negative. We also evaluated effects of CO(2) and temperature on light inhibition of dark respiration. Consistent with our hypothesis, NEE was a smaller C source in EC, not because EC increased photosynthesis but rather because of decreased respiration resulting in less C loss. Consistent with our hypothesis, NEE was more negative in ET because R(e) increased more than GPP. The light level that inhibited respiration varied seasonally with little difference among CO(2) and temperature treatments. In contrast, the degree of light inhibition of respiration was greater in AC than EC. In our system, respiration was the primary control on NEE, as EC and ET caused greater changes in respiration than photosynthesis.     

CO2储存通量估算方法对森林生态系统碳收支估测的影响

准确测定森林生态系统中CO₂储存通量(F_s)对于以涡动协方差(EC)法估算生态系统碳收支具有重要意义,而F_s不同算法引起的森林碳收支估测误差还未被全面评估。本研究利用2018年帽儿山落叶阔叶林的开路EC系统和8层CO₂/H₂O廓线系统(AP100, Campbell Scientific Inc., USA)数据,比较了2-min平均廓线(P_{2 min})、30-min平均廓线(P_{30 min})和30-min平均EC单点法(P_s)3种不同方法估算的F_s对净生态系统交换(NEE)、生态系统呼吸(R_e)和总初级生产力(GPP)估算结果的影响。结果表明: F_s估算方法对森林碳通量的影响总体上随时间尺度增大而不断增大,表明通量数据插补和拆分会进一步放大F_s估算方法的影响。在年尺度上,P_{2 min}法和P_s法的NEE分别比P_{30 min}法的低36.3%和29.4%;P_{2 min}法的R_e比P_{30 min}法和P_s法高8.7%;而P_{2 min}法的GPP比P_{30 min}法的高5.4%,P_s法则比P_{30 min}法的低2.1%。传统的P_{30 min}法忽略了CO₂浓度的瞬时变化,P_s法缺少林冠层内部CO₂浓度变化,因此两者低估了真实R_e。近似瞬时廓线的方法(2-min平均)具有更高的时间与空间分辨率,能够更加准确地估算非平坦地形和复杂冠层结构的森林碳收支,这对解决EC法在复杂条件下森林R_e和GPP低估、净碳汇高估具有重要启示。     

Stable Isotopes and Carbon Cycle Processes in Forests and Grasslands

Abstract: Scaling and partitioning are frequently two difficult challenges facing ecology today. With regard to ecosystem carbon balance studies, ecologists and atmospheric scientists are often interested in asking how fluxes of carbon dioxide scale across the landscape, region and continent. Yet at the same time, physiological ecologists and ecosystem ecologists are interested in dissecting the net ecosystem CO₂ exchange between the biosphere and the atmosphere to achieve a better understanding of the balance between photosynthesis and respiration within a forest. In both of these multiple-scale ecological questions, stable isotope analyses of carbon dioxide can play a central role in influencing our understanding of the extent to which terrestrial ecosystems are carbon sinks. In this synthesis, we review the theory and present field evidence to address isotopic scaling of CO₂ fluxes. We first show that the ¹³C isotopic signal which ecosystems impart to the atmosphere does not remain constant over time at either temporal or spatial scales. The relative balances of different biological activities and plant responses to stress result in dynamic changes in the ¹³C isotopic exchange between the biosphere and atmosphere, with both seasonal and stand-age factors playing major roles influencing the ¹³C biosphere-atmosphere exchange. We then examine how stable isotopes are used to partition net ecosystem exchange fluxes in order to calculate shifts in the balance of photosynthesis and respiration. Lastly, we explore how fundamental differences in the ¹⁸O isotopic gas exchange of forest and grassland ecosystems can be used to further partition terrestrial fluxes.     

Separation of net ecosystem exchange into assimilation and respiration using a light response curve approach: critical issues and global evaluation

The measured net ecosystem exchange (NEE) of CO₂ between the ecosystem and the atmosphere reflects the balance between gross CO₂ assimilation [gross primary production (GPP)] and ecosystem respiration (R_eco). For understanding the mechanistic responses of ecosystem processes to environmental change it is important to separate these two flux components. Two approaches are conventionally used: (1) respiration measurements made at night are extrapolated to the daytime or (2) light–response curves are fit to daytime NEE measurements and respiration is estimated from the intercept of the ordinate, which avoids the use of potentially problematic nighttime data. We demonstrate that this approach is subject to biases if the effect of vapor pressure deficit (VPD) modifying the light response is not included. We introduce an algorithm for NEE partitioning that uses a hyperbolic light response curve fit to daytime NEE, modified to account for the temperature sensitivity of respiration and the VPD limitation of photosynthesis. Including the VPD dependency strongly improved the model's ability to reproduce the asymmetric diurnal cycle during periods with high VPD, and enhances the reliability of R_eco estimates given that the reduction of GPP by VPD may be otherwise incorrectly attributed to higher R_eco. Results from this improved algorithm are compared against estimates based on the conventional nighttime approach. The comparison demonstrates that the uncertainty arising from systematic errors dominates the overall uncertainty of annual sums (median absolute deviation of GPP: 47 g C m^?2 yr^?1), while errors arising from the random error (median absolute deviation: ～2 g C m^?2 yr^?1) are negligible. Despite site‐specific differences between the methods, overall patterns remain robust, adding confidence to statistical studies based on the FLUXNET database. In particular, we show that the strong correlation between GPP and R_eco is not spurious but holds true when quasi‐independent, i.e. daytime and nighttime based estimates are compared.